Total 44,264 skills, AI & Machine Learning has 7037 skills
Showing 12 of 7037 skills
This skill should be used when the user asks to "audit a website for AI visibility", "scan a domain", "check AI readiness", "evaluate content quality", "run a Morphiq Scan", "check if a site is optimized for LLMs", or mentions scanning a website for LLM citation readiness. Performs a full AI visibility audit across 5 categories (agentic readiness, content quality, chunking & retrieval, query fanout, policy files) and scores the domain on a 100-point rubric.
Browse and search Bitrefill's 1,500+ gift cards, top-ups, and eSIMs across 180+ countries. Filter by brand, category, or country — no credentials required.
Deploys agent skill collections from any GitHub repository with a /skills folder to one or more distribution surfaces: GitHub releases, Claude Code marketplace, VS Code plugin marketplace, and Copilot CLI plugin marketplace. Handles pre-flight validation, conventional commit analysis, version bumping across surface configs, and surface-specific publishing with dry-run support. Use when releasing, publishing, or deploying a skills collection to any supported marketplace or creating a GitHub release for a skills repository. Don't use for deploying non-skill packages, npm modules, Docker images, or Azure resources.
Architecture patterns, design principles, and proven recipes for building robust robotics software. Use this skill when designing robot software architectures, choosing between behavioral frameworks, structuring perception-planning-control pipelines, implementing state machines, designing safety systems, or architecting multi-robot systems. Trigger whenever the user mentions behavior trees, finite state machines, subsumption architecture, sensor fusion, robot safety, watchdogs, heartbeats, graceful degradation, hardware abstraction layers, real-time constraints, or software architecture for robots. Also applies to sim-to-real transfer, digital twins, and robot fleet management.
Eino framework overview, concepts, and navigation. Use when a user asks general questions about Eino, needs help getting started, wants to understand the architecture, or is unsure which Eino skill to use. Eino is a Go framework for building LLM applications with components, orchestration graphs, and an agent development kit.
Eino orchestration with Graph, Chain, and Workflow. Use when a user needs to build multi-step pipelines, compose components into executable graphs, handle streaming between nodes, use branching or parallel execution, manage state with checkpoints, or understand the Runnable abstraction. Covers Graph (directed graph with cycles), Chain (linear sequential), and Workflow (DAG with field mapping).
Eino component selection, configuration, and usage. Use when a user needs to choose or configure a ChatModel, Embedding, Retriever, Indexer, Tool, Document loader/parser/transformer, Prompt template, or Callback handler. Covers all component interfaces and their implementations in eino-ext including OpenAI, Claude, Gemini, Ollama, Milvus, Elasticsearch, Redis, MCP tools, and more.
Expert assistant for BuilderBot (v1.4.0) — a TypeScript/JavaScript framework for building multi-platform chatbots (WhatsApp, Telegram, Instagram, Email, etc.). Use when creating or editing flows (addKeyword, addAnswer, addAction), wiring EVENTS, managing per-user state or globalState, configuring providers (Baileys, Meta, Telegram, Evolution, etc.) or databases (Mongo, Postgres, MySQL, JSON), implementing REST API endpoints (handleCtx, httpServer), debugging flow control (gotoFlow, endFlow, fallBack, idle, capture, flowDynamic), or handling blacklist logic. Architecture: Provider + Database + Flow.
Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores.
Browses TrueFoundry ML repositories and model registry. Lists repos, models, and artifacts with FQNs for use in other skills.
Adds OpenTelemetry-based tracing to applications via TrueFoundry's tracing platform (Traceloop SDK). Creates tracing projects, instruments Python/TypeScript code, and captures LLM calls and custom spans.
Deploys ML and LLM models on TrueFoundry with GPU inference servers (vLLM, TGI, NVIDIA NIM). Uses YAML manifests with `tfy apply`. Use when serving language models, deploying Hugging Face models, or hosting GPU-accelerated inference endpoints.